Title: A rating model simulation for risk analysis

Authors: Greta Falavigna

Addresses: Ceris-CNR, via Real Collegio 30, Moncaliere 10024, Turin, Italy; University of Eastern Piedmont 'Amedeo Avogadro', Novara, Italy

Abstract: This study analyses the situation of a bank that wants to create an Internal Rating System (IRB). A credit institute can decide to simulate rating judgements from an external rating agency, like Standard and Poor|s or Moody|s or Fitch Rating. This research compares different frameworks of neural networks, hybrid neuro-fuzzy model and logit/probit model, used to simulate the rating of an external agency. Initially, the models are divided into eight rating classes but the mean percentage error is big. Hence, a two-stage hybrid neuro-fuzzy framework is built, in which the model correctly distinguishes the firms into three macroclasses and then, for each macroclass, a hybrid model divides the firms into eight different classes. This two-stage framework provides good results.

Keywords: feed-forward neural networks; radial basis function; RBF; generalised regression neural network; Grnn; probabilistic neural network; Pnn; multinomial logit; probit; internal rating systems; default risk; complex system; risk assessment; neuro-fuzzy models; hybrid modelling; artifical neural networks; ANNs; insolvency.

DOI: 10.1504/IJBPM.2008.016642

International Journal of Business Performance Management, 2008 Vol.10 No.2/3, pp.269 - 299

Published online: 11 Jan 2008 *

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